Justin Gottschlich

Lead Artificial Intelligence Researcher, Programming Systems Research, Intel Labs

Principal Investigator and Co-Founder, Intel/NSF CAPA Research Center

Contact: justin.gottschlich@intel.com

I am the lead artificial intelligence researcher for programming systems research at Intel Lab. I'm also the principal investigator and co-founder of the joint Intel/NSF CAPA research center. I perform research in artificial intelligence with a focus on machine programming, anomaly detection, deep learning, and autonomous systems and maintain deep academic and industrial collaborations. I'm wildly interested in machine programming. In 2016, I co-founded the machine learning and programming languages (MAPL) workshop and was its program and general chair in 2017 and 2018, respectively.

I am an adjunct professor at the University of Colorado-Boulder and a lecturer at the University of Pennsylvania on anomaly detection for safe autonomy. I was previously the director of engineering at Machine Zone, where I oversaw the engineering of Game of War and Mobile Strike. When not doing research, I work on my online gaming software company, Nodeka, LLC, which I founded in 1999.

My (somewhat dated) CV is here. I have over 50 peer reviewed publications and issued patents (complete listing of my issued patents) with over 70 patents pending. I've given several dozen invited research presentations at places like Berkeley, BMW, IBM Research, Penn, Stanford, VMWare, UCLA, and UW. I've also been lucky enough to be recognized with a few best presentation awards (CGO '10, Raytheon ISaC '09, Intel SWPC '16).



Led machine programming patent harvesting session resulting in 31 new patent applications with over 40 Intel inventors.

Patent issued: "Extend GPU/CPU coherency to multi-GPU cores"

DATSA has been open sourced.

SysML whitepaper: "SysML: The New Frontier of Machine Learning Systems"

Invited talk, Stanford DAWN Retreat '19: "Machine Programming"

Patent issued: "Detecting root causes of use-after-free memory errors"

Patent issued: "Methods and systems to identify and reproduce concurrency violations in multi-threaded programs"

Invited talk to Dawn Song's research team at Berkeley: "Anomaly detection, machine programming, and other AI research at Intel"

Our "Precision and Recall for Time Series" NeurIPS paper has made a few different top paper reading lists. Here's one. Here's another.

Patent issued (milestone, 20th issued patent): "Programmable coarse grained and sparse matrix compute hardware with advanced scheduling."

Co-teaching with Insup Lee and James Weimer: CIS 700-002: Topics in Safe Autonomy, Spring 2019


Invited talk at UW's TVM Conference: "Machine Programming"

Invited talk at Intel's NeurIPS special luncheon: "Anomaly Detection: Today and Beyond"

NeurIPS spotlight talk: "Precision and Recall for Time Series"

NeurIPS 3-minute teaser video: "Precision and Recall for Time Series"

Intel Labs Division Recognition Award for creating and leading the Anomaly Detection IP Think Tank.

Invited talk at SPLASH-I: "The Future of AI: Machine Programmers and Their Necessary Self-Awareness"

Invited talk at Intel's Autonomous Driving Community of Practice Workshop: "Autonomous Vehicles and the Anomalous 1%"

GRASP / PRECISE Industry Symposium at University of Pennsylvania: "Deep Learning for Autonomous Driving" (video here)

MAPL presentation: "The Three Pillars of Machine Programming" (joint with MIT)

Program committee member, SysML 2019.

Invited talk at VMware Research: "Anomaly Detection for Practical Systems (and a Tiny Bit of Machine Programming)"

Special seminar at University of Pennsylvania: "The Future of Anomaly Detection" (slides forthcoming)

General Chair, Second ACM SIGPLAN Workshop on Machine Learning and Programming Languages (MAPL)


PhD co-advisor: Irina Calciu, Brown University - VMWare

PhD committee member: Wenjia Ruan, Lehigh University - Qualcomm

PhD committee member: Mohammad Mejbah ul Alam - Intel Labs